Comparing methods to detect cellular proteins on the surface of HIV-1 virions
Bibliographic record
Abstract
The surface of HIV-1 is embedded with numerous host-derived proteins. Characterizing these proteins can enhance knowledge of virus biology and potentially identify novel therapeutic targets. As many of these proteins are present in low abundance on virion surfaces, their identification can be hindered by inherent variables in the methods employed to detect them, including their varying assay sensitivities, sample processing, quantitative capacity, and experimental reproducibility. Here, we have compared the quantification of virion-incorporated proteins using conventional virus immunocapture assays and western blotting, alongside an emerging technique called flow virometry (FV). Using four different pseudovirus models that each express a human protein of interest (CD14, CD38, CD59 and CD162), we compared four experimental techniques for their ability to reliably quantify the incorporation of those four proteins onto virion surfaces. Our results shed light on the advantages and caveats of each technique for detecting virion-incorporated proteins and highlight the breadth in quantification for each technique under different experimental conditions. Protein detection with (FV) provided distinct advantages as it enabled highly reproducible quantifications, had the lowest sample requirements and reagent costs, and minimal hands-on experimental time. We additionally highlight some important considerations in experimental design when studying virion-incorporated proteins, such as the effect of different antibody clones, assay incubation times, and contributions of extracellular vesicles. Most importantly, our data illustrate the importance of using a combination of orthogonal approaches to detect virus-associated proteins, to enable reliable and reproducible quantification that accounts for individual assay biases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".